Search Results - "Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization" :: K.UTB vyhledávací portál

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Modeling Musical Genre Trajectories through Pathlet Learning

Published in Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization

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Soundtracks of Our Lives: How Age Influences Musical Preferences

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Conceptual Framework for Group Dynamics Modeling from Group Chat Interactions

Published in Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization

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Towards the Embodied Conversational Interview Agentic Service ELIAS: Development and Evaluation of a First Prototype

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Content-Based or Collaborative? Insights from Inter-List Similarity Analysis of ChatGPT Recommendations

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Towards Understanding Persuasive and Personalized Engagement for Human-AI Reliance

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Towards Automated Penetration Testing: Introducing LLM Benchmark, Analysis, and Improvements

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The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations

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Building Human-AI Reliance Through Cognitive Engagement and Exploratory AI Assistance

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Conducting Recommender Systems User Studies Using POPROX

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Procedural Memory Is Not All You Need

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Towards Cultural Preservation of Traditional Motion Knowledge through Automated Annotations with MoRTELaban

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Knowledge Graph-based User Models and Personalized Access for Cultural Heritage

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“Which vocational training program is best for me?” – Design of a recommender system for school students using large language models

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Towards Explainable Temporal User Profiling with LLMs

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Exploring the Potential of Multimodal Large Language Models for Question Answering on Artworks

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User Orientations and Stage-Specific Behaviors in E-commerce Exploratory Search: A Formative Study

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Exploring Responsible Use of Generative AI in Disaster Resilience for Indigenous Communities

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Enhancing Mathematical Reasoning in GPT-J Through Topic-Aware Prompt Engineering

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Bridging the Rift: A Critical Perspective on the Divergence in Group Recommender Systems Research

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